This article was published in the journal Control Engineering Practice [© Elsevier]. The definitive version is available at: http://dx.doi.org/10.1016/j.conengprac.2011.10.015Small-scale helicopters are very attractive for a wide range of civilian and military applications due to their unique features. However, the autonomous flight for small helicopters is quite challenging because they are naturally unstable, have strong nonlinearities and couplings, and are very susceptible to wind and small structural variations.\ud \ud A nonlinear optimal control scheme is proposed to address these issues. It consists of a nonlinear model predictive controller (MPC) and a nonlinear disturbance observer. First, an analytical solution of the MPC is developed based on the nominal model under the assumption that all disturbances are measurable. Then, a nonlinear disturbance observer is designed to estimate the influence of the external force/torque introduced by wind turbulences, unmodelled dynamics and variations of the helicopter dynamics. The global asymptotic stability of the composite controller has been established through stability analysis. Flight tests including hovering under wind gust and performing very challenging pirouette have been carried out to demonstrate the performance of the proposed control scheme

This article was published in the journal IEEE Transactions on Reliability [© IEEE] and is also available at: http://ieeexplore.ieee.org/servlet/opac?punumber=24 Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.This paper is concerned with ETA (event-tree analysis)\ud where the branch point event causes are defined using fault\ud trees. Attention is on the nontrivial situation where there are dependencies\ud amongst the branch point events. The dependencies\ud are due to component-failures in more than one of the fault trees.\ud In these situations the analysis methods based on traditional FTA\ud (fault-tree analysis) are inaccurate & inefficient.\ud The inaccuracies are not consistent across the outcome events. If\ud frequency predictions calculated in this way are then used in a risk\ud assessment then the relative riskswould be distorted and could lead\ud to resources being used inappropriately to reduce the overall risk.\ud A new approach using BDD (binary decision diagram) is described\ud which addresses these deficiencies

Railway importance in the transportation industry is increasing continuously, due to the growing demand of both passenger travel and transportation of goods. However, more than 35% of the 300,000 railway bridges across Europe are over 100-years old, and their reliability directly impacts the reliability of the railway network. This increased demand may lead to higher risk associated with their unexpected failures, resulting safety hazards to passengers and increased whole life cycle cost of the asset. Consequently, one of the most important aspects of evaluation of the reliability of the overall railway transport system is bridge structural health monitoring, which can monitor the health state of the bridge by allowing an early detection of failures. Therefore, a fast, safe and cost-effective recovery of the optimal health state of the bridge, where the levels of element degradation or failure are maintained efficiently, can be achieved. In this article, after an introduction to the desired features of structural health monitoring, a review of the most commonly adopted bridge fault detection methods is presented. Mainly, the analysis focuses on model-based finite element updating strategies, non-model-based (data-driven) fault detection methods, such as artificial neural network, and Bayesian belief network–based structural health monitoring methods. A comparative study, which aims to discuss and compare the performance of the reviewed types of structural health monitoring methods, is then presented by analysing a short-span steel structure of a railway bridge. Opportunities and future challenges of the fault detection methods of railway bridges are highlighted.

The UK rail industry remains a safe, reliable and competitive alternative to other forms of transport. Maintaining such an ageing, increasingly utilized, railway network with limited financial resources is a major challenge. By utilizing data on the UK network track geometry collected over six years, along with available records of maintenance performed during this period, the effects of tamping on track geometry degradation have been analysed. This paper presents a Weibull approach to analyse the distributions of times for the track geometry to degrade to specified states following maintenance. Track quality is given by the standard deviation of the averaged vertical position of the rails taken over a 35 m wavelength. The quality of the track geometry is also investigated after tamping has been performed. The results of this study show that traffic speed and the maintenance history of any line have an effect on geometry deterioration. Furthermore, support is provided to the argument that tamping damages track ballast.

The use of binary decision diagrams (BDDs) in fault tree analysis provides both an accurate and efficient means of analysing a system. There is a problem, however, with the conversion process of the fault tree to the BDD. The variable ordering scheme chosen for the construction of the BDD has a crucial effect on its resulting size and previous research has failed to identify any scheme that is capable of producing BDDs for all fault trees. This paper proposes an analysis strategy aimed at increasing the likelihood of obtaining a BDD for any given fault tree, by ensuring the associated calculations are as efficient as possible. The method implements simplification techniques, which are applied to the fault tree to obtain a set of ‘minimal’ subtrees, equivalent to the original fault tree structure. BDDs are constructed for each, using ordering schemes most suited to their particular characteristics. Quantitative analysis is performed simultaneously on the set of BDDs to obtain the top event probability, the system unconditional failure intensity and the criticality of the basic events.